scholarly journals ВИКОРИСТАННЯ ПРОГРАМИ «SIMPLY TAGGING» ДЛЯ ПРОГНОЗУВАННЯ ГУСТОТИ ПТАХІВ У ГНІЗДОВИХ БІОТОПАХ

2012 ◽  
Vol 2 (2) ◽  
pp. 94
Author(s):  
О. V. Matsyura ◽  
М. V. Matsyura ◽  
А. А. Zimaroyeva
Keyword(s):  
Moving Average ◽  
Regression Line ◽  
Correlation Coefficients ◽  
Real Data ◽  
Data Series ◽  
Autoregressive Moving Average ◽  
Indirect Methods ◽  
Bird Populations ◽  
Random Fluctuations

<p>For the analysis of long-term observations data on dynamics of bird populations the most suitable methods could be the stochastic processes. Abundance (density) of birds is calculated on the integrated area of studied habitats. Using the method of autocorrelation the correlogram of changes in number of birds drawn during the study period in all the area. After that, the calculation of the autocorrelation coefficients and partial autocorrelation are performed. The most appropriate model is the mixed autoregressive moving average (ARIMA). Ecological significance of autoregressive parameters is to display the frequency of changes in the number of birds in the seasonal and long-term aspects. The sliding average is one of the simplest methods, which allows reject the random fluctuations of the empirical regression line. Validation of the model could be conducted on truncated data series (10 years). The forecast is calculated for the next two years and compared with empirical data. Calculation of correlation coefficients between the real data and the forecast is performed using non-parametric Spearman correlation coefficient. The residual rows of selected models are estimated by residual correlogram. The constructed model can be used to analyze and forecast the number of birds in breeding biotopes.</p> <p><em>Keywords: analysis, density, indirect methods, birds, Simply Tagging.</em></p> <p> </p>

Continuous Autoregressive Moving Average Models

2021 ◽  
pp. 118-141
Author(s):  
Yakup Ari
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Discrete Time ◽  
Moving Average ◽  
Likelihood Estimation ◽  
Real Data ◽  
Autoregressive Moving Average ◽  
The Difference

The financial time series have a high frequency and the difference between their observations is not regular. Therefore, continuous models can be used instead of discrete-time series models. The purpose of this chapter is to define Lévy-driven continuous autoregressive moving average (CARMA) models and their applications. The CARMA model is an explicit solution to stochastic differential equations, and also, it is analogue to the discrete ARMA models. In order to form a basis for CARMA processes, the structures of discrete-time processes models are examined. Then stochastic differential equations, Lévy processes, compound Poisson processes, and variance gamma processes are defined. Finally, the parameter estimation of CARMA(2,1) is discussed as an example. The most common method for the parameter estimation of the CARMA process is the pseudo maximum likelihood estimation (PMLE) method by mapping the ARMA coefficients to the corresponding estimates of the CARMA coefficients. Furthermore, a simulation study and a real data application are given as examples.

scholarly journals Investigation of a Multiple-Timescale Turbulence-Transport Coupling Method in the Presence of Random Fluctuations

Plasma ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 126-143
Author(s):  
Jeffrey Parker ◽  
Lynda LoDestro ◽  
Alejandro Campos
Keyword(s):  
Moving Average ◽  
Point Of View ◽  
Coupling Method ◽  
Autoregressive Moving Average ◽  
Specific Level ◽  
Autoregressive Moving Average Model ◽  
Stochastic Fluctuations ◽  
Moving Average Model ◽  
Turbulence Transport ◽  
Random Fluctuations

One route to improved predictive modeling of magnetically confined fusion reactors is to couple transport solvers with direct numerical simulations (DNS) of turbulence, rather than with surrogate models. An additional challenge presented by coupling directly with DNS is the inherent fluctuations in the turbulence, which limit the convergence achievable in the transport solver. In this article, we investigate the performance of one numerical coupling method in the presence of turbulent fluctuations. To test a particular numerical coupling method for the transport solver, we use an autoregressive-moving-average model to generate stochastic fluctuations efficiently with statistical properties resembling those of a gyrokinetic simulation. These fluctuations are then added to a simple, solvable problem, and we examine the behavior of the coupling method. We find that monitoring the residual as a proxy for the error can be misleading. From a pragmatic point of view, this study aids us in the full problem of transport coupled to DNS by predicting the amount of averaging required to reduce the fluctuation error and obtain a specific level of accuracy.

Bivariate exponential and geometric autoregressive and autoregressive moving average models

1988 ◽  
Vol 20 (4) ◽  
pp. 798-821 ◽  
Author(s):  
H. W. Block ◽  
N. A. Langberg ◽  
D. S. Stoffer
Keyword(s):  
Moving Average ◽  
Real Data ◽  
Autoregressive Moving Average ◽  
Positive Dependence ◽  
Data Set ◽  
Associated Random Variables ◽  
Bivariate Exponential ◽  
Moving Average Models ◽  
Special Cases ◽  
Autoregressive Moving Average Models

We present autoregressive (AR) and autoregressive moving average (ARMA) processes with bivariate exponential (BE) and bivariate geometric (BG) distributions. The theory of positive dependence is used to show that in various cases, the BEAR, BGAR, BEARMA, and BGARMA models consist of associated random variables. We discuss special cases of the BEAR and BGAR processes in which the bivariate processes are stationary and have well-known bivariate exponential and geometric distributions. Finally, we fit a BEAR model to a real data set.

Band-limited spectral estimation of autoregressive-moving-average processes

1986 ◽  
Vol 23 (A) ◽  
pp. 143-155
Author(s):  
P. J. Thomson
Keyword(s):  
Moving Average ◽  
Spectral Estimation ◽  
Preliminary Investigation ◽  
Asymptotic Properties ◽  
Low Frequency ◽  
Real Data ◽  
Estimation Procedure ◽  
Autoregressive Moving Average ◽  
Recording Device ◽  
Band Limited

Consider an autoregressive-moving-average process of given order where it is known that a number of moving-average roots are of unit modulus. Such a situation might arise, for example, when a time series has been differenced to induce stationarity by removing a non-stationary polynomial or seasonal trend. A band-limited spectral estimation procedure is proposed for estimating the coefficients of such a process and the asymptotic properties of the estimators investigated. The asymptotic theory is illustrated with reference to simulated and real data. A preliminary investigation of the use of Akaike's AIC criterion and this procedure to determine the number of roots of unit modulus (in the case where this is unknown) is also carried out by means of simulation. The proposed band-limited spectral estimation procedure can also be used to take account of other possible effects met in practice. These include, for example, the band-limited response of a recording device or trend-contaminated low-frequency components.

scholarly journals Recovering Long-term Aerosol Optical Depth Series (1976–2012) from an Astronomical Potassium-based Resonance Scattering Spectrometer

2014 ◽  
Vol 7 (4) ◽  
pp. 4093-4121 ◽  
Author(s):  
A. Barreto ◽  
E. Cuevas ◽  
P. Pallé ◽  
P. M. Romero ◽  
F. Almansa ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Correlation Coefficients ◽  
Resonant Scattering ◽  
Resonance Scattering ◽  
Data Series ◽  
Long Term Stability ◽  
Filter Radiometer ◽  
Scattered Component

Abstract. A 37 year long-term series of monochromatic Aerosol Optical Depth (AOD) has been recovered from solar irradiance measurements performed with the solar spectrometer Mark-I, deployed at Izaña mountain since 1976. The instrument operation is based on the method of resonant scattering, which presents a long-term stability and high precision in comparison to other instruments based on interference filters. However, it has been specifically designed as a reference instrument for helioseismology, and its ability to determine AOD from transmitted and scattered monochromatic radiation at 769.9 nm inside a potassium vapor cell in the presence of a permanent magnetic field is evaluated in this paper. Particularly, the use of an exposed mirrors arrangement to collect sunlight as well as the Sun-laboratory velocity dependence of the scattered component introduces some inconveniences when we perform the instrument's calibration. We have solved this problem using a quasi-continuous Langley calibration technique and a refinement procedure to correct for calibration errors as well as for the fictitious diurnal cycle on AOD data. Our results showed that calibration errors associated to the quasi-continuous Langley technique are not dependent on aerosol load, provided aerosol concentration remains constant throughout the day, assuring the validity of this technique for those periods with relatively high aerosol content required to calibrate the scattered component. The comparative analysis between the recovered AOD dataset from Mark-I and collocated quasi-simultaneous data from Cimel AErosol RObotic NETwork (AERONET) and Precision Filter Radiometer (PFR) instruments showed an absolute mean bias &amp;leq; 0.01 in the 11 year and 12 year comparison, respectively. High correlation coefficients between AERONET/Mark-I and PFR/Mark-I pairs confirmed a very good linear relationship between instruments, proving that recovered AOD data series from Mark-I can be used together PFR and AERONET AOD data to build a long-term AOD data series at Izaña site (1976–now), suitable for future analysis of aerosols trends and inter-annual variability. Finally, the AOD preliminary trend analysis in the 29 year period from 1984 to 2012 with Mark-I AOD revealed no significant trends. However, we detected a negative significant trend of 0.047 decade−1 during the period 1984–1993.

scholarly journals Trender i antal ringmärkta fåglar av fyrtio arter på Stora Fjäderäggs fågelstation, nordöstra Sverige, 1985–2014

Ornis Svecica ◽  
2015 ◽  
Vol 25 (3–4) ◽  
pp. 94-104
Author(s):  
Lars Edenius ◽  
Niklas Lindberg Alseryd ◽  
Sören Wulff
Keyword(s):  
Data Series ◽  
Positive Trend ◽  
Northern Sweden ◽  
Southern Sweden ◽  
Breeding Bird ◽  
Bird Populations ◽  
Willow Tit ◽  
Bird Fauna ◽  
Few Data

Very few data series are available on the long-term development of the bird fauna in northern Sweden. This kind of data is of great interest as there are recent signs that bird populations in northern Sweden are developing less favourable than in southern Sweden. We present trends in annual capture rates of 40 frequently ringed species at Stora Fjäderägg Bird Observatory, NE Sweden, autumns 1985–2014. Significant positive and negative trends were found in ten and eight species, respectively. Great tit, Chaffinch and Long-tailed Tit showed the strongest increases, whereas Northern Wheatear, Willow Tit and Bluethroat decreased the most. There was a significant negative trend in capture index for short-distance migrants and a positive trend for irruptive species/partial migrants. For many species, particularly those that were decreasing, the trends at Stora Fjäderägg are similar to population trends seen in Swedish and Finnish breeding bird surveys. For these species the trends at Stora Fjäderägg could be indicative of long-term population changes.

scholarly journals Two-Stage Spatiotemporal Time Series Modelling Approach for Rice Yield Prediction & Advanced Agroecosystem Management

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2502
Author(s):  
Santosha Rathod ◽  
Amit Saha ◽  
Rahul Patil ◽  
Gabrijel Ondrasek ◽  
Channappa Gireesh ◽  
...  
Keyword(s):  
Time Series ◽  
Rice Yield ◽  
Moving Average ◽  
National Level ◽  
Autoregressive Moving Average ◽  
Two Stage ◽  
Promising Alternative ◽  
Time Series Modelling ◽  
Linear And Nonlinear

A robust forecast of rice yields is of great importance for medium-to-long-term planning and decision-making in cereal production, from regional to national level. Incorporation of spatially correlated adjacent effects in forecasting models in general, results in accurate forecast. The Space Time Autoregressive Moving Average (STARMA) is the most popular class of model in linear spatiotemporal time series modelling. However, STARMA cannot process nonlinear spatiotemporal relationships in datasets. Alternately, Time Delay Neural Network (TDNN) is a most popular machine learning algorithm to model the nonlinear pattern in data. To overcome these limitations, two-stage STARMA approach was developed to predict rice yield in some of the most intensive national rice agroecosystems in India. The Mean Absolute Percentage Errors value of proposed STARMA-II approach is lower compared to Autoregressive Moving Average (ARIMA) and STARMA model in all examined districts, while the Diebold-Mariano test confirmed that STARMA-II model is significantly different from classical approaches. The proposed STARMA-II approach is promising alternative to classical linear and nonlinear spatiotemporal time series models for estimating mixed linear and nonlinear patterns and can be advanced tool for mid-to-long-term sustainable planning and management of crop yields and patterns in agroecosystems, i.e., food supply and demand from local to regional levels.

Band-limited spectral estimation of autoregressive-moving-average processes

1986 ◽  
Vol 23 (A) ◽  
pp. 143-155 ◽  
Author(s):  
P. J. Thomson
Keyword(s):  
Moving Average ◽  
Spectral Estimation ◽  
Preliminary Investigation ◽  
Asymptotic Properties ◽  
Low Frequency ◽  
Real Data ◽  
Estimation Procedure ◽  
Autoregressive Moving Average ◽  
Recording Device ◽  
Band Limited

Consider an autoregressive-moving-average process of given order where it is known that a number of moving-average roots are of unit modulus. Such a situation might arise, for example, when a time series has been differenced to induce stationarity by removing a non-stationary polynomial or seasonal trend. A band-limited spectral estimation procedure is proposed for estimating the coefficients of such a process and the asymptotic properties of the estimators investigated. The asymptotic theory is illustrated with reference to simulated and real data. A preliminary investigation of the use of Akaike's AIC criterion and this procedure to determine the number of roots of unit modulus (in the case where this is unknown) is also carried out by means of simulation.The proposed band-limited spectral estimation procedure can also be used to take account of other possible effects met in practice. These include, for example, the band-limited response of a recording device or trend-contaminated low-frequency components.

scholarly journals Recovering long-term aerosol optical depth series (1976–2012) from an astronomical potassium-based resonance scattering spectrometer

2014 ◽  
Vol 7 (12) ◽  
pp. 4103-4116 ◽  
Author(s):  
A. Barreto ◽  
E. Cuevas ◽  
P. Pallé ◽  
P. M. Romero ◽  
C. Guirado ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Correlation Coefficients ◽  
Resonant Scattering ◽  
Resonance Scattering ◽  
Data Series ◽  
Data Set ◽  
Long Term Stability ◽  
Filter Radiometer

Abstract. A 37-year long-term series of monochromatic aerosol optical depth (AOD) has been recovered from solar irradiance measurements performed with the solar spectrometer Mark-I, deployed at Izaña mountain since 1976. The instrument operation is based on the method of resonant scattering, which affords wavelength absolute reference and stability (long-term stability and high precision) in comparison to other instruments based purely on interference filters. However, it has been specifically designed as a reference instrument for helioseismology, and its ability to determine AOD from transmitted and scattered monochromatic radiation at 769.9 nm inside a potassium vapour cell in the presence of a permanent magnetic field is evaluated in this paper. Particularly, the use of an exposed mirror arrangement to collect sunlight as well as the Sun–laboratory velocity dependence of the scattered component introduces some important inconveniences to overcome when we perform the instrument's calibration. We have solved this problem using a quasi-continuous Langley calibration technique and a refinement procedure to correct for calibration errors as well as for the fictitious diurnal cycle on AOD data. Our results showed similar calibration errors retrieved by means of this quasi-continuous Langley technique applied in different aerosol load events (from 0.04 to 0.3), provided aerosol concentration remains constant throughout the calibration interval. It assures the validity of this technique when it is applied in those periods with relatively high aerosol content. The comparative analysis between the recovered AOD data set from the Mark-I and collocated quasi-simultaneous data from the Cimel-AErosol RObotic NETwork (AERONET) and Precision Filter Radiometer (PFR) instruments showed an absolute mean bias &amp;leq; 0.01 in the 10- and 12-year comparison, respectively. High correlation coefficients between AERONET and Mark-I and PFR/Mark-I pairs confirmed a very good linear relationship between instruments, proving that recovered AOD data series from Mark-I can be used together with PFR and AERONET AOD data to build a long-term AOD data series at the Izaña site (1976–now), suitable for future analysis of aerosols trends and inter-annual variability. Finally, the AOD preliminary trend analysis in the 29-year period from 1984 to 2012 with Mark-I AOD revealed no significant trends.

Polar Ocean Observation Buoys Seal Failure Research Based on ARMA Theory

2014 ◽  
Vol 577 ◽  
pp. 709-712
Author(s):  
Shi Ming Wang ◽  
Qing Li ◽  
Zhun Ren
Keyword(s):  
Moving Average ◽  
Arma Model ◽  
The Arctic ◽  
Autoregressive Moving Average ◽  
Mechanism Analysis ◽  
Seal Failure ◽  
Status Monitoring ◽  
Seawater Corrosion ◽  
Polar Ocean

Due to the special conditions of the Arctic climate, ocean observation buoys long-term work in harsh environments, such as seawater corrosion, temperature, pressure and other factors to buoy a higher sealing requirements, ocean snorkeling standard sealing system status monitoring and fault diagnosis mechanism analysis using autoregressive-moving average ARMA model for pressure measurement error problem buoy mathematical modeling can be applied to solve the pressure seal failure buoy measurement problems caused errors.

Sign in / Sign up

Export Citation Format

Share Document